The main aims of the project are to understand current disease behavior in terms of evolutionary forces, and consequently predict plausible future changes, using complex computer simulation models supported by detailed experimental observations from a laboratory system. Human and animal diseases should all be close to an evolutionary stable strategy, such that their live-history characteristics (e.g. transmission rate, incubation and infectious period) cannot be invaded by other competing strains. We seek to determine the underlying processes and hence understand the range of disease behaviors observed. This basic understanding can then be extended in two applied directions. The first is to determine the likely evolution of current diseases in response to changes in social (and sexual) mixing patterns. The second is to examine the evolution of drug or antibiotic resistance and methods to control its spread. This work therefore has important health related consequences, predicting which diseases are likely to evolve into a major public health concern in the coming decades and how to best conserve the dwindling number of effective antibiotics for which there are no resistant diseases. The basic tool for this research will be sophisticated computer simulation for disease spread and evolution on a network defined by potential transmission routes. Where possible these simulations will be strengthened by more generic mathematical models, such as pair-wise or metapopulation equations. One severe limitation with all evolutionary model to date is their dependence on weakly supported trade-offs between the various life-history elements; a common assumption is that virulence increases with transmission rate. This project will over-come this scarcity of data by examining the trade-offs present in bacteria-phage interactions which can be carefully controlled in the laboratory. Bacteria-phage systems are the most common example of host-disease interactions in nature, and as such have a profound impact in many ecological settings. Bacteria can be cultured in a variety of environments (to simulation different human transmission networks), and the life-cycle of both bacteria and phage are sufficiently rapid that large evolutionary changes can be readily observed. The types of trade-offs and constraints seen in the experimental systems can then be used to formulated more biologically mechanistic and more accurate computer models of disease evolution in higher organisms.